CN113985491A - Gravity field model refinement method and system based on multi-source data - Google Patents
Gravity field model refinement method and system based on multi-source data Download PDFInfo
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Abstract
The invention discloses a gravity field model refinement method and system based on multi-source data. The multi-source data integration module can reduce the multi-source disturbance gravity value to an average elevation gravity equipotential surface, and is beneficial to improving the precision of the gravity field model; the gravity field model refinement module can eliminate data errors caused by data fluctuation, and further refine the gravity field model.
Description
Technical Field
The invention relates to the technical field of surveying and mapping, in particular to a gravity field model refinement method and system based on multi-source data.
Background
The earth gravity field is one of important physical properties of the earth, and the study of the earth gravity field and the time variation thereof is a necessary way for people to know the earth more deeply. CHAMP, GRACE, GOGE and GRACE-FO provide massive gravity observation data for earth gravity inversion, and simultaneously have marine height measurement data, ground gravity observation data and aviation gravity observation data, and the fusion of the multi-source gravity observation data is of great help for improving the overall accuracy of an earth gravity field model. The earth gravity field model with high precision and high spatial resolution can provide important basic information for predicting natural disasters, revealing environmental changes and the like. The method has practical and positive significance for improving the precision efficiency and precision by digitalizing and intelligentizing the multi-source gravity data fusion and the gravity field model refinement.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a gravity field model refinement method and system based on multi-source data, and the precision of a gravity field model is improved.
First aspect
The invention provides a gravity field model refinement method based on multi-source data, which comprises the following steps:
acquiring data of a high-fractional digital elevation model and reference gravity field model;
preprocessing the data of the high-fraction digital elevation model and the reference gravity field model to obtain a low-pass digital elevation model and a discrete disturbance gravity value, and removing local terrain influence and ultrahigh-order disturbance gravity from the low-pass digital elevation model and the discrete disturbance gravity value by adopting a removal recovery method to obtain residual disturbance gravity;
performing weighted basis function interpolation gridding on the residual disturbance gravity to generate a residual disturbance gravity grid model;
restoring the removed local topographic influence and the removed ultrahigh-order disturbance gravity on the residual disturbance gravity grid model by using a removal restoration method, and reducing the local topographic influence and the ultrahigh-order disturbance gravity on an equipotential surface of average elevation gravity to obtain an equipotential surface disturbance gravity grid model;
removing local topographic influence and model disturbance gravity in the equipotential surface disturbance gravity grid model by adopting a removal recovery method to obtain an equipotential surface residual disturbance gravity grid, and performing integration processing on the equipotential surface residual disturbance gravity grid to obtain a residual gravity field model;
and recovering local terrain influence and model disturbance gravity on the residual gravity field model by adopting a removal recovery method to obtain a refined gravity field model.
Preferably, after the step of obtaining the refined gravity field model, the method further comprises: and checking the refined gravity field model.
Preferably, the method further comprises the steps of: constructing a geodetic high grid model, a geodetic level high grid model and an equipotential level geodetic high grid digital model;
the integral processing is carried out on the potential plane residual disturbance gravity grid, and the obtained residual gravity field model comprises the following steps:
obtaining a residual ground elevation abnormal grid through the generalized Hotinee numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
obtaining a residual geodetic surface high grid through the generalized Hotinee numerical integration calculation based on the geodetic surface high grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic surface high grid digital model;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic height grid model, an equipotential surface residual disturbance gravity grid and an equipotential surface geodetic height grid digital model to obtain a residual ground perpendicular line deviation grid;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic level high grid model, an equipotential level residual disturbance gravity grid and an equipotential level geodetic level high grid digital model to obtain a residual plumb line deviation grid;
obtaining a residual ground disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
and obtaining a residual disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic level high grid model, the equipotential level residual disturbance gravity grid and the equipotential level geodetic level high grid digital model.
Preferably, the constructing of the equipotential surface geodetic high grid digital model comprises:
and (4) carrying out equipotential surface construction of the appointed point based on residual disturbance gravity to obtain an equipotential surface geodetic high grid digital model.
Second aspect of the invention
The invention provides a gravity field model refinement system based on multi-source data, which comprises a multi-source data integration module and a gravity field model refinement module;
the multi-source data integration module is used for:
acquiring data of a high-fractional digital elevation model and reference gravity field model;
preprocessing the data of the high-fraction digital elevation model and the reference gravity field model to obtain a low-pass digital elevation model and a discrete disturbance gravity value, and removing local terrain influence and ultrahigh-order disturbance gravity from the low-pass digital elevation model and the discrete disturbance gravity value by adopting a removal recovery method to obtain residual disturbance gravity;
performing weighted basis function interpolation gridding on the residual disturbance gravity to generate a residual disturbance gravity grid model;
restoring the removed local topographic influence and the removed ultrahigh-order disturbance gravity on the residual disturbance gravity grid model by using a removal restoration method, and reducing the local topographic influence and the ultrahigh-order disturbance gravity on an equipotential surface of average elevation gravity to obtain an equipotential surface disturbance gravity grid model;
the gravity field model refinement module is configured to:
removing local topographic influence and model disturbance gravity in the equipotential surface disturbance gravity grid model by adopting a removal recovery method to obtain an equipotential surface residual disturbance gravity grid, and performing integration processing on the equipotential surface residual disturbance gravity grid to obtain a residual gravity field model;
and recovering local terrain influence and model disturbance gravity on the residual gravity field model by adopting a removal recovery method to obtain a refined gravity field model.
The gravity field model processing system further comprises a checking module used for checking the refined gravity field model.
The multi-source data integration module is also used for constructing a geodetic high grid model, a geodetic level high grid model and an equipotential surface geodetic high grid digital model;
the integral processing is carried out on the potential plane residual disturbance gravity grid, and the obtained residual gravity field model comprises the following steps:
obtaining a residual ground elevation abnormal grid through the generalized Hotinee numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
obtaining a residual geodetic surface high grid through the generalized Hotinee numerical integration calculation based on the geodetic surface high grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic surface high grid digital model;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic height grid model, an equipotential surface residual disturbance gravity grid and an equipotential surface geodetic height grid digital model to obtain a residual ground perpendicular line deviation grid;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic level high grid model, an equipotential level residual disturbance gravity grid and an equipotential level geodetic level high grid digital model to obtain a residual plumb line deviation grid;
obtaining a residual ground disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
and obtaining a residual disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic level high grid model, the equipotential level residual disturbance gravity grid and the equipotential level geodetic level high grid digital model.
The construction of the equipotential surface geodetic high grid digital model comprises the following steps:
and (4) carrying out equipotential surface construction of the appointed point based on residual disturbance gravity to obtain an equipotential surface geodetic high grid digital model.
The invention has the beneficial effects that:
(1) the multi-source disturbance gravity value can be reduced to the average elevation gravity equipotential surface, and great help is provided for improving the precision of the gravity field model;
(2) the data error caused by data fluctuation can be eliminated, and the precision of the gravity field model is further improved.
Drawings
In order to more clearly illustrate the detailed description of the invention or the technical solutions in the prior art, the drawings that are needed in the detailed description of the invention or the prior art will be briefly described below. Throughout the drawings, like elements or portions are generally identified by like reference numerals. In the drawings, elements or portions are not necessarily drawn to scale.
Fig. 1 is a schematic flowchart of a gravity field model refinement method based on multi-source data according to an embodiment of the present invention;
fig. 2 is a schematic structural diagram of a gravity field model refinement system based on multi-source data according to an embodiment of the present invention.
Detailed Description
Embodiments of the present invention will be described in detail below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and therefore are only examples, and the protection scope of the present invention is not limited thereby.
It is to be noted that, unless otherwise specified, technical or scientific terms used herein shall have the ordinary meaning as understood by those skilled in the art to which the invention pertains.
As shown in fig. 1, an embodiment of the present invention provides a gravity field model refinement method based on multi-source data, including the following steps:
s1, acquiring data of a high-score digital elevation model and a reference gravity field model; the high-score digital elevation model and the reference gravity field model data can be acquired through websites such as geographic information of Chinese academy of sciences and cultural science and technology industry bases;
s2, preprocessing data of the high-fraction digital elevation model and the reference gravity field model to obtain a low-pass digital elevation model and a discrete disturbance gravity value, and removing local topographic influence and ultrahigh-order disturbance gravity from the low-pass digital elevation model and the discrete disturbance gravity value by adopting a removal recovery method to obtain residual disturbance gravity; wherein, the preprocessing comprises gross error elimination, data weighting and range determination;
s3, carrying out weighted basis function interpolation gridding on the residual disturbance gravity to generate a residual disturbance gravity grid model;
s4, restoring the removed local terrain influence and the removed ultrahigh-order disturbance gravity on the residual disturbance gravity grid model by using a removal restoration method, and reducing the local terrain influence and the ultrahigh-order disturbance gravity on an equipotential surface of average elevation gravity to obtain an equipotential surface disturbance gravity grid model;
s5, removing local terrain influence and model disturbance gravity in the equipotential surface disturbance gravity grid model by adopting a removal recovery method to obtain an equipotential surface residual disturbance gravity grid, and performing integration processing on the equipotential surface residual disturbance gravity grid to obtain a residual gravity field model;
s6, restoring local terrain influence and model disturbance gravity on the residual gravity field model by adopting a removal restoration method to obtain a refined gravity field model;
s7, checking the refined gravity field model; specifically, the precision of the refined gravity field model is evaluated by using a GPS or a level model.
The gravity field model refinement method based on multi-source data further comprises the following steps: constructing a geodetic high grid model, a geodetic level high grid model and an equipotential level geodetic high grid digital model; wherein, the geodetic high grid model and the geodetic level high grid model are common models in the field; the construction of the equipotential surface geodetic high grid digital model comprises the following steps: and (4) carrying out equipotential surface construction of the appointed point based on residual disturbance gravity to obtain an equipotential surface geodetic high grid digital model.
The method for obtaining the residual gravity field model by integrating the potential plane residual disturbance gravity grid comprises the following steps:
obtaining a residual ground elevation abnormal grid through the generalized Hotinee numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
obtaining a residual geodetic surface high grid through the generalized Hotinee numerical integration calculation based on the geodetic surface high grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic surface high grid digital model;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic height grid model, an equipotential surface residual disturbance gravity grid and an equipotential surface geodetic height grid digital model to obtain a residual ground perpendicular line deviation grid;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic level high grid model, an equipotential level residual disturbance gravity grid and an equipotential level geodetic level high grid digital model to obtain a residual plumb line deviation grid;
obtaining a residual ground disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
and obtaining a residual disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic level high grid model, the equipotential level residual disturbance gravity grid and the equipotential level geodetic level high grid digital model. And obtaining a residual gravity field model, namely the residual gravity field model comprises a residual ground elevation abnormal grid, a residual geodetic level height grid, a residual ground vertical line deviation grid, a residual ground disturbance gravity grid and a residual disturbance gravity grid.
In the embodiment of the invention, the ground level surface is a gravity equipotential surface which is closest to or even coincident with the average sea level; geodetic height is the elevation data under a reference ellipsoid system, i.e. the distance from a ground point by the intersection of the reference ellipsoid normal through the ground point and the ellipsoid.
The embodiment of the invention also provides a gravity field model refinement system based on multi-source data, which comprises a multi-source data integration module and a gravity field model refinement module, as shown in fig. 2;
the multi-source data integration module is used for:
acquiring data of a high-fractional digital elevation model and reference gravity field model;
preprocessing data of the high-fraction digital elevation model and the reference gravity field model to obtain a low-pass digital elevation model and a discrete disturbance gravity value, and removing local terrain influence and ultrahigh-order disturbance gravity from the low-pass digital elevation model and the discrete disturbance gravity value by adopting a removal recovery method to obtain residual disturbance gravity;
carrying out weighted basis function interpolation gridding on the residual disturbance gravity to generate a residual disturbance gravity grid model;
restoring the removed local topographic influence and the removed ultrahigh-order disturbance gravity on the residual disturbance gravity grid model by using a removal restoration method, and reducing the local topographic influence and the ultrahigh-order disturbance gravity on an equipotential surface of average elevation gravity to obtain an equipotential surface disturbance gravity grid model;
the gravity field model refinement module is used for:
removing local topographic influence and model disturbance gravity in the equipotential surface disturbance gravity grid model by adopting a removal recovery method to obtain an equipotential surface residual disturbance gravity grid, and performing integration processing on the equipotential surface residual disturbance gravity grid to obtain a residual gravity field model;
and recovering local terrain influence and model disturbance gravity on the residual gravity field model by adopting a removal recovery method to obtain a refined gravity field model.
The system also comprises a checking module used for checking the refined gravity field model.
The multi-source data integration module is also used for constructing a geodetic high grid model, a geodetic level high grid model and an equipotential surface geodetic high grid digital model; the construction of the equipotential surface geodetic high grid digital model comprises the following steps: and (4) carrying out equipotential surface construction of the appointed point based on residual disturbance gravity to obtain an equipotential surface geodetic high grid digital model.
Integrating the potential plane residual disturbance gravity grid to obtain a residual gravity field model, wherein the residual gravity field model comprises the following steps:
obtaining a residual ground elevation abnormal grid through the generalized Hotinee numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
obtaining a residual geodetic surface high grid through the generalized Hotinee numerical integration calculation based on the geodetic surface high grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic surface high grid digital model;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic height grid model, an equipotential surface residual disturbance gravity grid and an equipotential surface geodetic height grid digital model to obtain a residual ground perpendicular line deviation grid;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic level high grid model, an equipotential level residual disturbance gravity grid and an equipotential level geodetic level high grid digital model to obtain a residual plumb line deviation grid;
obtaining a residual ground disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
and obtaining a residual disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic level high grid model, the equipotential level residual disturbance gravity grid and the equipotential level geodetic level high grid digital model.
According to the gravity field model refinement method and system based on multi-source data, provided by the invention, the data can be more effectively utilized through the fusion of the multi-source data; and obtaining a refined gravity field model by eliminating data errors caused by data fluctuation.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications and substitutions do not depart from the spirit and scope of the present invention, and they should be construed as being included in the following claims and description.
Claims (8)
1. A gravity field model refinement method based on multi-source data is characterized by comprising the following steps:
acquiring data of a high-fractional digital elevation model and reference gravity field model;
preprocessing the data of the high-fraction digital elevation model and the reference gravity field model to obtain a low-pass digital elevation model and a discrete disturbance gravity value, and removing local terrain influence and ultrahigh-order disturbance gravity from the low-pass digital elevation model and the discrete disturbance gravity value by adopting a removal recovery method to obtain residual disturbance gravity;
performing weighted basis function interpolation gridding on the residual disturbance gravity to generate a residual disturbance gravity grid model;
restoring the removed local topographic influence and the removed ultrahigh-order disturbance gravity on the residual disturbance gravity grid model by using a removal restoration method, and reducing the local topographic influence and the ultrahigh-order disturbance gravity on an equipotential surface of average elevation gravity to obtain an equipotential surface disturbance gravity grid model;
removing local topographic influence and model disturbance gravity in the equipotential surface disturbance gravity grid model by adopting a removal recovery method to obtain an equipotential surface residual disturbance gravity grid, and performing integration processing on the equipotential surface residual disturbance gravity grid to obtain a residual gravity field model;
and recovering local terrain influence and model disturbance gravity on the residual gravity field model by adopting a removal recovery method to obtain a refined gravity field model.
2. The gravity field model refinement method based on multi-source data according to claim 1, further comprising, after the step of obtaining a refined gravity field model: and checking the refined gravity field model.
3. The gravity field model refinement method based on multi-source data according to claim 1, further comprising the steps of: constructing a geodetic high grid model, a geodetic level high grid model and an equipotential level geodetic high grid digital model;
the integral processing is carried out on the potential plane residual disturbance gravity grid, and the obtained residual gravity field model comprises the following steps:
obtaining a residual ground elevation abnormal grid through the generalized Hotinee numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
obtaining a residual geodetic surface high grid through the generalized Hotinee numerical integration calculation based on the geodetic surface high grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic surface high grid digital model;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic height grid model, an equipotential surface residual disturbance gravity grid and an equipotential surface geodetic height grid digital model to obtain a residual ground perpendicular line deviation grid;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic level high grid model, an equipotential level residual disturbance gravity grid and an equipotential level geodetic level high grid digital model to obtain a residual plumb line deviation grid;
obtaining a residual ground disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
and obtaining a residual disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic level high grid model, the equipotential level residual disturbance gravity grid and the equipotential level geodetic level high grid digital model.
4. The gravity field model refinement method based on multi-source data according to claim 3, characterized in that the construction of the equipotential surface geodetic high grid digital model comprises:
and (4) carrying out equipotential surface construction of the appointed point based on residual disturbance gravity to obtain an equipotential surface geodetic high grid digital model.
5. A gravity field model refinement system based on multi-source data is characterized by comprising a multi-source data integration module and a gravity field model refinement module;
the multi-source data integration module is used for:
acquiring data of a high-fractional digital elevation model and reference gravity field model;
preprocessing the data of the high-fraction digital elevation model and the reference gravity field model to obtain a low-pass digital elevation model and a discrete disturbance gravity value, and removing local terrain influence and ultrahigh-order disturbance gravity from the low-pass digital elevation model and the discrete disturbance gravity value by adopting a removal recovery method to obtain residual disturbance gravity;
performing weighted basis function interpolation gridding on the residual disturbance gravity to generate a residual disturbance gravity grid model;
restoring the removed local topographic influence and the removed ultrahigh-order disturbance gravity on the residual disturbance gravity grid model by using a removal restoration method, and reducing the local topographic influence and the ultrahigh-order disturbance gravity on an equipotential surface of average elevation gravity to obtain an equipotential surface disturbance gravity grid model;
the gravity field model refinement module is configured to:
removing local topographic influence and model disturbance gravity in the equipotential surface disturbance gravity grid model by adopting a removal recovery method to obtain an equipotential surface residual disturbance gravity grid, and performing integration processing on the equipotential surface residual disturbance gravity grid to obtain a residual gravity field model;
and recovering local terrain influence and model disturbance gravity on the residual gravity field model by adopting a removal recovery method to obtain a refined gravity field model.
6. The gravity field model refinement system based on multi-source data according to claim 5, further comprising a checking module for checking the refined gravity field model.
7. The gravity field model refinement system based on multi-source data of claim 5, characterized in that said multi-source data integration module is further configured to construct a geodetic high grid model, a geodetic level high grid model and an equipotential level geodetic high grid digital model;
the integral processing is carried out on the potential plane residual disturbance gravity grid, and the obtained residual gravity field model comprises the following steps:
obtaining a residual ground elevation abnormal grid through the generalized Hotinee numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
obtaining a residual geodetic surface high grid through the generalized Hotinee numerical integration calculation based on the geodetic surface high grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic surface high grid digital model;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic height grid model, an equipotential surface residual disturbance gravity grid and an equipotential surface geodetic height grid digital model to obtain a residual ground perpendicular line deviation grid;
calculating by using a generalized Vening Meinesz disturbance gravity integral based on a geodetic level high grid model, an equipotential level residual disturbance gravity grid and an equipotential level geodetic level high grid digital model to obtain a residual plumb line deviation grid;
obtaining a residual ground disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic height grid model, the equipotential surface residual disturbance gravity grid and the equipotential surface geodetic height grid digital model;
and obtaining a residual disturbance gravity grid through field element Possion numerical integration calculation based on the geodetic level high grid model, the equipotential level residual disturbance gravity grid and the equipotential level geodetic level high grid digital model.
8. The gravity field model refinement system based on multi-source data of claim 7, wherein the construction of the equipotential surface geodetic high grid digital model comprises:
and (4) carrying out equipotential surface construction of the appointed point based on residual disturbance gravity to obtain an equipotential surface geodetic high grid digital model.
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